Optimization

Geometry Optimization

The energy and properties of a single molecular geometry of a
system are of limited interest, especially as the size of the system
increases. In consequence, a wide range of techniques has been developed
to explore the conformations that are accessible to a system. Some
of the most basic of these are local geometry optimization
algorithms that aim to locate structures of low potential energy.
pDynamo has a number of geometry optimization methods, one of which — a
conjugate gradient algorithm — is illustrated in the program
Example10.py:

The program employs a semi-empirical QC method to geometry
optimize a conformation of the bALA molecule. After the optimization,
the difference in energy between the starting, unoptimized and final,
optimized structures is printed along with their RMS coordinate
deviation.

Exercises

The bALA molecule has a number of different stable
conformations. One way of exploring these is to generate a
two-dimensional map of the molecule's conformational space as a function
of its φ and ψ dihedral angles. Using pDynamo's dihedral soft
constraint
capability, generate such a φ/ψ map by performing
geometry optimizations with different constrained values of the φ and ψ
angles. Identify the stable regions on the surface and the low energy
paths that go between them. Examples of how to use soft constraints may be found in the programs Example18.py and Example23.py.